Baghdad Atmani

Dynamic knowledge mapping guided by data mining: Application on Healthcare

The capitalization of know-how, knowledge management, and the control of the constantly growing information mass has become the new strategic challenge for organizations that aim to capture the entire wealth of knowledge (tacit and explicit). Thus, knowledge mapping is a means of (cognitive) navigation to access the resources of the strategic heritage knowledge of an organization. In this paper, we present a new mapping approach based on the Boolean modeling of critical domain knowledge and on the use of different data sources via the data mining technique in order to improve the process of acquiring knowledge explicitly. To evaluate our approach, we have initiated a process of mapping that is guided by machine learning that is artificially operated in the following two stages: data mining and automatic mapping. Data mining is be initially run from an induction of Boolean case studies (explicit). The mapping rules are then used to automatically improve the Boolean model of the mapping of critical knowledge

SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED ON GENETIC ALGORITHM

Feature Selection (FS) has become the focus of much research on decision support systems areas for which datasets with tremendous number of variables are analyzed. In this paper we present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapped Bayes Naive (BN) based FS. Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA generates in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final set of attribute contains the most relevant feature model that increases the accuracy. The algorithm in this case produces 85.50% classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is correspondingly compared with other FS algorithms. The Obtained results have shown very promising outcomes for the diagnosis of CAD.

Using a Cellular Automaton to Extract Medical Information from Clinical Reports

An important amount of clinical data concerning the medical history of a patient is in the form of clinical reports that are written by doctors. They describe patients, their pathologies, their personal and medical histories, findings made during interviews or during procedures, and so forth. They represent a source of precious information that can be used in several applications such as research information to diagnose new patients, epidemiological studies, decision support, statistical analysis, and data mining. But this information is difficult to access, as it is often in unstructured text form. To make access to patient data easy, our research aims to develop a system for extracting information from unstructured text. In a previous work, a rule-based approach is applied to a clinical reports corpus of infectious diseases to extract structured data in the form of named entities and properties. In this paper, we propose the use of a Boolean inference engine, which is based on a cellular automaton, to do extraction. Our motivation to adopt this Boolean modeling approach is twofold: first optimize storage, and second reduce the response time of the entities extraction.

A New Approach for Coronary Artery Diseases Diagnosis Based on Genetic Algorithm

Feature Selection FS has become the motivation of much research on decision support systems areas for which datasets with large number of features are analyzed. This paper presents a new method for the diagnosis of Coronary Artery Diseases CAD founded on Genetic Algorithm GA wrapper Bayes Naive BN. Initially, thirteen attributes were involved in predicting CAD. In GA-BN algorithm, GA produces in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final result set of attribute holds the most pertinent feature model that increases the accuracy. The accuracy results showed that the algorithm produces 85.50% classification accuracy in the diagnosis of CAD. Therefore, the strength of the Algorithm is then compared with other machine learning algorithms such as Support Vector Machine SVM, Multi-Layer Perceptron MLP and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Then, the GA wrapper BN Algorithm is similarly compared with other FS algorithms. The Obtained results have shown very favorable outcomes for the diagnosis of CAD.

Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease

The use of data mining approaches in medicine and medical science has become necessary especially with the evolution of these approaches and their contributions medical decision support. Coronary artery disease CAD touches millions of people all over the world including a major portion in Algeria. However, much advancement has been done in medical science, but the early detection of CAD is still a challenge for prevention. Although, the early detection of CAD is a prevention challenge for clinicians. The subject of this paper is to propose new clinical decision support system CDSS for evaluating risk of CAD called CADSS. In this paper, the authors describe the characteristics of clinical decision support systems CDSSs for the diagnosis of CAD. The aim of this study is to explain the clinical contribution of CDSSs for medical decision-making and compare data mining techniques used for their implementation. Then, they describe their new fuzzy logic-based approach for detecting CAD at an early stage. Rules were extracted using a data mining technique and validated by experts, and the fuzzy expert system was used to handle the uncertainty present in the medical field. This work presents the main risk factors responsible for CAD and presents the designed CASS. The developed CADSS leads to 94.05% of accuracy, and its effectiveness was compared with different CDSS.

Extraction of Priority Rules for Boolean Induction in Distributed Manufacturing Control

In reactive manufacturing control, the allocation of resources for tasks is achieved in real time. When a resource becomes available it chooses one of the tasks in its queue. This choice is made according to priority rules which are designed to optimize costs, time, etc. In this paper, the aim is to exploit a Job Shop scheduling log and simulations in order to extract knowledge enabling one to create rules for the selection of priority rules. These rules are implemented in a CASI cellular automaton. Firstly, symbolic modelling of the scheduling process is exploited to generate a decision tree from the log and simulations. Secondly, decision rules are extracted to select priority rules for execution in a specific situation. Finally, the rules are integrated in CASI which implements the decisional module of agents in a distributed manufacturing control system.

Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic

In the medical field, experts’ knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning.

Obtaining Optimal Bio-PEPA Model Using Association Rules: Approach Applied to Tuberculosis Case Study

The computational modelling has been applied in several works, which exert considerable positive impact, particularly in epidemiological field. However, modelling epidemics is very sensitive where selecting appropriate feature and model structure is challenging task for experts and epidemiologists. To overcome this limitation, we presented in previous work a methodology combining computational modelling and decision tree techniques. The approach has been validated on tuberculosis case study. Therefore, as comparative study, we propose here to apply association rules algorithms. The results indicate the epidemiological relevance of the extracted rules. Thus, the enhanced Bio-PEPA model demonstrates the robustness of the proposed approach.

Clustering to Enhance Case-Based Reasoning

In this article, we propose an approach to improve CBR processing mainly in its retrieval task. A major difficulty arise when founding several similar cases and consequently several solutions, hence a choice must be done involving an appropriate strategy focusing the best solution. This main difficulty has a direct impact on the adaptation task. To overcome this limitation many works related to the retrieval task were conducted as hybridizing CBR with data mining methods. Through this study, we provide a combining approach using CBR and clustering to reduce the search space in the retrieval step. The objective is to consider only the most interesting cases and the most interesting solution to support decision and provide an intelligent strategy that enables decision makers to have the best decision aid. We also present some preliminary results and suggestions to extend our approach.

Multiple priority dispatching rules for the job shop scheduling problem

In this paper we focus on the Job Shop Scheduling Problem (JSSP) using Priority Dispatching Rules. Simulation model for makespan optimization is proposed using different Dispatching Rules (DR) for each machine in the shop floor. Collected results are used for learning base construction. This database will be used to develop an inference model able to select the best DR for every new scheduling problem. This preliminary study shows advantages of using different DR and also saving progress JSSP data.